# Scales, Axes & Coordinate systems

## Scales

Within **ggplot** there are a bunch of `scale`

s that control how a plot maps data values to the visual values of an aesthetic. To change the mapping, add a custom scale. These scales look something like (the **ggplot2**-cheatsheet is helpful as well):

`scale_x_continuous`

; change your x-axis when your x-variable is continuous (`scale_y_continuous`

for y-axis)`scale_x_discrete`

; change your x-axis when your x-variable is categorical (`scale_y_discrete`

for y-axis)`scale_*_identity`

; use data values as visual values (e.g.,`scale_colour_identity`

when your variable in`colour = variable`

contains the names of colours). * can be`fill`

,`colour`

,`shape`

,`size`

`scale_*_manual(values = c())`

; map discrete values to manually chosen visual values (e.g.,`scale_fill_manual(values = c("4" = "blue", "r" = "red", "f" = "black"))`

* can be`fill`

,`colour`

,`shape`

,`size`

.

### scale_x_continuous

Let’s see what `scale_x_continuous`

and `scale_y_continuous`

can do.

We’ll change features from this graph

#### Breaks

#### Zooming in and out with limits

For instance, often we want to zoom in or out. Let’s zoom in on the y-axis and zoom out on the x-axis.

```
ggplot(mpg, aes(x = hwy, y = cty)) +
geom_point() +
scale_x_continuous(limits = c(0, 75)) +
scale_y_continuous(limits = c(20, 30))
```

```
## Warning: Removed 180 rows containing missing values or values outside the scale
## range (`geom_point()`).
```

It works! Importantly, though, we must realise that there are many datapoints not visualized currently, as the warning message in **R** already suggests.

Using the limits in this way can be a bit dangerous. Let’s see why in an example with mean + error plots:

```
ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_bar(stat = "summary", fun = "mean") +
geom_errorbar(stat = "summary", fun.data = "mean_se") +
geom_point(colour = "lightblue")
```

Now let’s see what happens if we give limits that exclude some points

```
ggplot(mpg, aes(x = manufacturer, y = cty)) +
geom_bar(stat = "summary", fun = "mean") +
geom_errorbar(stat = "summary", fun.data = "mean_se") +
geom_point(colour = "lightblue") +
scale_y_continuous(limits = c(0, 25))
```

```
## Warning: Removed 8 rows containing non-finite outside the scale range (`stat_summary()`).
## Removed 8 rows containing non-finite outside the scale range (`stat_summary()`).
```

```
## Warning: Removed 8 rows containing missing values or values outside the scale
## range (`geom_point()`).
```

This is certainly not what we had in mind! We have changed our underlying data; all 8 datapoints that were larger than 25 were removed and *then* the data were plotted. Compare the bar and error for Honda and Volkswagen in the two graphs.

An alternative and preferred way of doing this, is doing it in the following way (also see `coord_cartesian`

below):

#### Expand

ggplot adds some space to the bottom and the side of your axes. If you do not want this, use the `expand()`

function. Compare the following two graphs:

```
ggplot(mpg, aes(x = displ, y = cty)) +
geom_point() +
scale_x_continuous(limits = c(0, 8)) +
scale_y_continuous(limits = c(0, 36))
```

```
ggplot(mpg, aes(x = displ, y = cty)) +
geom_point() +
scale_x_continuous(limits = c(0, 8), expand = c(0, 0)) +
scale_y_continuous(limits = c(0, 36), expand = c(0, 0))
```

But do note that you are risking throwing away datapoints again if you specify limits that exlude particular datapoints.

#### Transformations

There are also functions that allow you to transform the scales of the axes. For instance, you can think of having a logarithmic scale, or a square-root-transformed scale.

Look closely at the x-axis. We have transformed the x-axis. This works much better for variables that have more skewed distributions.

Another example:

What happened?

Another example:

What happened?

### scale_x_discrete

When your x-axis is discrete (or categorical), you can use `scale_x_discrete`

to change all sorts of relevant stuff. Let’s see how that works for the following graph:

### scale_colour_identity

Identity uses that values that are specified within a variable. Let’s create a column with colour names for the different types of `drv`

.

```
library(tidyverse)
mpg <- mpg %>%
mutate(colour = case_when(
drv == "f" ~ "blue",
drv == "r" ~ "orange",
drv == "4" ~ "green"
))
```

We can now use this variable:

Fill works too:

### scale_fill_manual

We could have created the above graph also with `scale_fill_manual`

:

```
ggplot(mpg, aes(x = drv, y = cty, fill = colour)) +
geom_boxplot() +
scale_fill_manual(values = c("blue", "green", "orange"))
```

Again, better to be a bit specific, to be more sure that the colour is added to the right group:

```
ggplot(mpg, aes(x = drv, y = cty, fill = drv)) +
geom_boxplot() +
scale_fill_manual(values = c("f" = "blue", "4" = "green", "r" = "orange"))
```

Let’s try to control the order and labels of the legend!

## Coordinate systems

**ggplot** has several coordinate systems, you’ll mostly use the default `coord_cartesian`

, but also `coord_fixed`

and `coord_flip`

might come in handy.